






"""simple docstring"""


import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
		from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__=None     ,      a__=None     ,      a__=None     ,      a__=None     ,      a__=None     ,      ):





						'''simple docstring'''






						if attention_mask is None:
												lowerCAmelCase   :Dict						=			input_ids.ne(config.pad_token_id )
						if decoder_attention_mask is None:
												lowerCAmelCase   :Any						=			decoder_input_ids.ne(config.pad_token_id )
						if head_mask is None:
												lowerCAmelCase   :int						=			torch.ones(config.encoder_layers     ,      config.encoder_attention_heads     ,      device=a__ )
						if decoder_head_mask is None:
												lowerCAmelCase   :Any						=			torch.ones(config.decoder_layers     ,      config.decoder_attention_heads     ,      device=a__ )
						if cross_attn_head_mask is None:
												lowerCAmelCase   :Optional[int]						=			torch.ones(config.decoder_layers     ,      config.decoder_attention_heads     ,      device=a__ )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}




class 						__UpperCamelCase     :
					def __init__(							self							:	int     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Optional[Any]=13     , UpperCAmelCase							:	Any=7     , UpperCAmelCase							:	Union[str, Any]=True     , UpperCAmelCase							:	List[str]=False     , UpperCAmelCase							:	Optional[Any]=99     , UpperCAmelCase							:	List[str]=16     , UpperCAmelCase							:	Union[str, Any]=2     , UpperCAmelCase							:	List[str]=4     , UpperCAmelCase							:	Dict=4     , UpperCAmelCase							:	Dict="relu"     , UpperCAmelCase							:	Optional[Any]=0.1     , UpperCAmelCase							:	List[Any]=0.1     , UpperCAmelCase							:	List[Any]=0.0     , UpperCAmelCase							:	Optional[int]=0.0     , UpperCAmelCase							:	Tuple=20     , UpperCAmelCase							:	Dict=2     , UpperCAmelCase							:	List[Any]=1     , UpperCAmelCase							:	int=0     , )	->       Any:
											lowerCAmelCase   :str						=			parent
											lowerCAmelCase   :List[str]						=			batch_size
											lowerCAmelCase   :Optional[Any]						=			seq_length
											lowerCAmelCase   :Dict						=			is_training
											lowerCAmelCase   :Any						=			use_labels
											lowerCAmelCase   :Dict						=			vocab_size
											lowerCAmelCase   :Union[str, Any]						=			hidden_size
											lowerCAmelCase   :List[Any]						=			num_hidden_layers
											lowerCAmelCase   :Dict						=			num_attention_heads
											lowerCAmelCase   :Any						=			intermediate_size
											lowerCAmelCase   :Union[str, Any]						=			hidden_act
											lowerCAmelCase   :str						=			hidden_dropout_prob
											lowerCAmelCase   :Optional[Any]						=			attention_probs_dropout_prob
											lowerCAmelCase   :int						=			encoder_layerdrop
											lowerCAmelCase   :List[str]						=			decoder_layerdrop
											lowerCAmelCase   :List[str]						=			max_position_embeddings
											lowerCAmelCase   :Dict						=			eos_token_id
											lowerCAmelCase   :List[str]						=			pad_token_id
											lowerCAmelCase   :Any						=			bos_token_id
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Optional[int]:
											lowerCAmelCase   :Any						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )
											lowerCAmelCase   :Dict						=			self.eos_token_id  # Eos Token
											lowerCAmelCase   :Union[str, Any]						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )

											# we need to clamp the input ids here to avoid having pad token in between
											# this is because for M2M100 the position_ids are prepared such that
											# all pad tokens have pos id = 2 and rest are between 2..seq_length
											# and the seq_length here is seq_length - num_pad_tokens
											# but when using past, there is no way of knowing if the past input ids had
											# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
											# position_ids being off by num_pad_tokens in past input
											lowerCAmelCase   :int						=			input_ids.clamp(self.pad_token_id + 1   )
											lowerCAmelCase   :Optional[Any]						=			decoder_input_ids.clamp(self.pad_token_id + 1   )

											lowerCAmelCase   :str						=			self.get_config()
											lowerCAmelCase   :Tuple						=			prepare_mam_aaa_inputs_dict(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   )
											return config, inputs_dict
					def        UpperCAmelCase__ (							self							:	str   )	->       Tuple:
											return MaMaaaConfig(
											    vocab_size=self.vocab_size     , d_model=self.hidden_size     , encoder_layers=self.num_hidden_layers     , decoder_layers=self.num_hidden_layers     , encoder_attention_heads=self.num_attention_heads     , decoder_attention_heads=self.num_attention_heads     , encoder_ffn_dim=self.intermediate_size     , decoder_ffn_dim=self.intermediate_size     , dropout=self.hidden_dropout_prob     , attention_dropout=self.attention_probs_dropout_prob     , encoder_layerdrop=self.encoder_layerdrop     , decoder_layerdrop=self.decoder_layerdrop     , max_position_embeddings=self.max_position_embeddings     , eos_token_id=self.eos_token_id     , bos_token_id=self.bos_token_id     , pad_token_id=self.pad_token_id     , )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       List[str]:
											lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			self.prepare_config_and_inputs()
											return config, inputs_dict
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Optional[int]   )	->       Optional[int]:
											lowerCAmelCase   :Optional[int]						=			MaMaaaModel(config=UpperCAmelCase   ).get_decoder().to(UpperCAmelCase   ).eval()
											lowerCAmelCase   :Optional[Any]						=			inputs_dict['input_ids']
											lowerCAmelCase   :Any						=			inputs_dict['attention_mask']
											lowerCAmelCase   :Any						=			inputs_dict['head_mask']

											# first forward pass
											lowerCAmelCase   :Any						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , head_mask=UpperCAmelCase     , use_cache=UpperCAmelCase   )

											lowerCAmelCase	, lowerCAmelCase   :str						=			outputs.to_tuple()

											# create hypothetical multiple next token and extent to next_input_ids
											lowerCAmelCase   :Optional[int]						=			ids_tensor((self.batch_size, 3)     , config.vocab_size   )
											lowerCAmelCase   :int						=			ids_tensor((self.batch_size, 3)     , 2   )

											# append to next input_ids and
											lowerCAmelCase   :int						=			torch.cat([input_ids, next_tokens]     , dim=-1   )
											lowerCAmelCase   :str						=			torch.cat([attention_mask, next_attn_mask]     , dim=-1   )

											lowerCAmelCase   :List[str]						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase   )['last_hidden_state']
											lowerCAmelCase   :Optional[int]						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , past_key_values=UpperCAmelCase   )[
											    'last_hidden_state'
											]

											# select random slice
											lowerCAmelCase   :List[Any]						=			ids_tensor((1,)     , output_from_past.shape[-1]   ).item()
											lowerCAmelCase   :int						=			output_from_no_past[:, -3:, random_slice_idx].detach()
											lowerCAmelCase   :int						=			output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(UpperCAmelCase     , UpperCAmelCase     , atol=1e-2   )   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Tuple   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			MaMaaaModel(config=UpperCAmelCase   ).to(UpperCAmelCase   ).eval()
											lowerCAmelCase   :str						=			model(**UpperCAmelCase   )

											lowerCAmelCase   :Optional[int]						=			outputs.encoder_last_hidden_state
											lowerCAmelCase   :Dict						=			outputs.last_hidden_state

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowerCAmelCase   :List[str]						=			model.get_encoder()
																	encoder.save_pretrained(UpperCAmelCase   )
																	lowerCAmelCase   :Optional[int]						=			MaMaaaEncoder.from_pretrained(UpperCAmelCase   ).to(UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			encoder(inputs_dict['input_ids']     , attention_mask=inputs_dict['attention_mask']   )[
											    0
											]

											self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3   )

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowerCAmelCase   :List[str]						=			model.get_decoder()
																	decoder.save_pretrained(UpperCAmelCase   )
																	lowerCAmelCase   :Optional[int]						=			MaMaaaDecoder.from_pretrained(UpperCAmelCase   ).to(UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			decoder(
											    input_ids=inputs_dict['decoder_input_ids']     , attention_mask=inputs_dict['decoder_attention_mask']     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=inputs_dict['attention_mask']     , )[0]

											self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3   )




@require_torch
class 						__UpperCamelCase     (     UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				str      =  (
					    (
					        MaMaaaModel,
					        MaMaaaForConditionalGeneration,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase_    :				Tuple      =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
					lowercase_    :				Dict      =  (
					    {
					        """conversational""": MaMaaaForConditionalGeneration,
					        """feature-extraction""": MaMaaaModel,
					        """summarization""": MaMaaaForConditionalGeneration,
					        """text2text-generation""": MaMaaaForConditionalGeneration,
					        """translation""": MaMaaaForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase_    :				Optional[int]      =  True
					lowercase_    :				Union[str, Any]      =  True
					lowercase_    :				str      =  False
					lowercase_    :				Tuple      =  False
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Union[str, Any]   )	->       List[Any]:
											if pipeline_test_casse_name == "TranslationPipelineTests":
																	# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																	# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																	return True

											return False
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Dict:
											lowerCAmelCase   :Any						=			MaMaaaModelTester(self   )
											lowerCAmelCase   :str						=			ConfigTester(self     , config_class=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int   )	->       int:
											self.config_tester.run_common_tests()
					def        UpperCAmelCase__ (							self							:	Any   )	->       Tuple:
											lowerCAmelCase	, lowerCAmelCase   :List[str]						=			self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																	lowerCAmelCase   :Optional[int]						=			model_class(UpperCAmelCase   )

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							model.save_pretrained(UpperCAmelCase   )
																							lowerCAmelCase	, lowerCAmelCase   :Tuple						=			model_class.from_pretrained(UpperCAmelCase     , output_loading_info=UpperCAmelCase   )
																	self.assertEqual(info['missing_keys']     , []   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[Any]:
											lowerCAmelCase   :Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Optional[int]:
											lowerCAmelCase   :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       int:
											lowerCAmelCase	, lowerCAmelCase   :str						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																	lowerCAmelCase   :Tuple						=			model_class(UpperCAmelCase   )
																	model.to(UpperCAmelCase   )
																	model.eval()

																	lowerCAmelCase   :List[str]						=			copy.deepcopy(self._prepare_for_class(UpperCAmelCase     , UpperCAmelCase   )   )

																	if not self.is_encoder_decoder:
																							lowerCAmelCase   :int						=			inputs['input_ids']
																							del inputs["input_ids"]
																	else:
																							lowerCAmelCase   :List[Any]						=			inputs['input_ids']
																							lowerCAmelCase   :Optional[Any]						=			inputs.get('decoder_input_ids'     , UpperCAmelCase   )
																							del inputs["input_ids"]
																							inputs.pop('decoder_input_ids'     , UpperCAmelCase   )

																	lowerCAmelCase   :Any						=			model.get_input_embeddings()
																	if not self.is_encoder_decoder:
																							lowerCAmelCase   :Union[str, Any]						=			wte(UpperCAmelCase   )
																	else:
																							lowerCAmelCase   :Tuple						=			wte(UpperCAmelCase   )
																							lowerCAmelCase   :Union[str, Any]						=			wte(UpperCAmelCase   )

																	with torch.no_grad():
																							model(**UpperCAmelCase   )[0]
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											lowerCAmelCase	, lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase   :Union[str, Any]						=			input_dict['input_ids']
											lowerCAmelCase   :int						=			input_ids.ne(1   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			MaMaaaForConditionalGeneration(UpperCAmelCase   ).eval().to(UpperCAmelCase   )
											if torch_device == "cuda":
																	model.half()
											model.generate(UpperCAmelCase     , attention_mask=UpperCAmelCase   )
											model.generate(num_beams=4     , do_sample=UpperCAmelCase     , early_stopping=UpperCAmelCase     , num_return_sequences=3   )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						return torch.tensor(a__     ,      dtype=torch.long     ,      device=a__ )


__SCREAMING_SNAKE_CASE   =   1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 						__UpperCamelCase     (     unittest.TestCase						):
					@cached_property
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict:
											return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       List[str]:
											lowerCAmelCase   :Dict						=			MaMaaaModel.from_pretrained('facebook/m2m100_418M'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]   )
											lowerCAmelCase   :Union[str, Any]						=			_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]   )
											lowerCAmelCase   :Tuple						=			prepare_mam_aaa_inputs_dict(model.config     , UpperCAmelCase     , UpperCAmelCase   )
											with torch.no_grad():
																	lowerCAmelCase   :Optional[int]						=			model(**UpperCAmelCase   )[0]
											lowerCAmelCase   :List[Any]						=			torch.Size((1, 11, 1024)   )
											self.assertEqual(output.shape     , UpperCAmelCase   )
											# change to expected output here
											lowerCAmelCase   :List[str]						=			torch.tensor(
											    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]     , device=UpperCAmelCase   )
											self.assertTrue(torch.allclose(output[:, :3, :3]     , UpperCAmelCase     , atol=UpperCAmelCase   )   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Union[str, Any]:
											lowerCAmelCase   :Optional[Any]						=			MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'   ).to(UpperCAmelCase   )

											# change to intended input
											lowerCAmelCase   :List[str]						=			_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]   )
											lowerCAmelCase   :List[Any]						=			_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]   )
											lowerCAmelCase   :Dict						=			prepare_mam_aaa_inputs_dict(model.config     , UpperCAmelCase     , UpperCAmelCase   )
											with torch.no_grad():
																	lowerCAmelCase   :Optional[Any]						=			model(**UpperCAmelCase   )[0]
											lowerCAmelCase   :str						=			torch.Size((1, 11, model.config.vocab_size)   )
											self.assertEqual(output.shape     , UpperCAmelCase   )
											# change to expected output here
											lowerCAmelCase   :List[Any]						=			torch.tensor(
											    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]     , device=UpperCAmelCase   )
											self.assertTrue(torch.allclose(output[:, :3, :3]     , UpperCAmelCase     , atol=UpperCAmelCase   )   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[Any]:
											lowerCAmelCase   :List[Any]						=			MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Any						=			MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'     , src_lang='fr'     , tgt_lang='en'   )

											lowerCAmelCase   :Union[str, Any]						=			[
											    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
											    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
											    'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
											    ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
											    ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
											]

											# The below article tests that we don't add any hypotheses outside of the top n_beams
											lowerCAmelCase   :int						=			tokenizer(UpperCAmelCase     , padding=UpperCAmelCase     , return_tensors='pt'   )

											lowerCAmelCase   :Dict						=			model.generate(
											    input_ids=dct['input_ids'].to(UpperCAmelCase   )     , attention_mask=dct['attention_mask'].to(UpperCAmelCase   )     , num_beams=5     , forced_bos_token_id=tokenizer.get_lang_id('en'   )     , )

											lowerCAmelCase   :Optional[Any]						=			[
											    'The NSA case highlights the total absence of intelligence debate',
											    'I think there are two levels of response from the French government.',
											    'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
											    ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
											    ' communications in France.',
											]

											lowerCAmelCase   :int						=			tokenizer.batch_decode(
											    hypotheses_batch.tolist()     , clean_up_tokenization_spaces=UpperCAmelCase     , skip_special_tokens=UpperCAmelCase   )
											assert generated == expected_en







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")